Predictive analytic models can be based on data extracted from a product's historical performance. A predictive model can predict trends and behavior patterns to create maintenance schedules that both improve the product's field reliability and minimize its downtime. To predict a future event, a predictive model can be based on past occurrences, component reliability, and/or engineering predictions.
It can be desirable to make assessment and/or predictions regarding the operation of a real world physical system, such as an electro-mechanical system—e.g., an aircraft turbine engine. The predictive model can be used to predict a condition of the system, or a portion of the system, to help make maintenance decisions, budget predictions, etc. Even with improvements in sensor and computer technologies, however, accurately making such assessments and/or predictions can be a difficult task.
A predictive model can include parameters and dimensions of the real-world physical system, which can be updated by historical maintenance records and/or data from sensors embedded in the system itself. A robust predictive model can consider multiple components of a system, each having its own micro-characteristics and not just average measures of a plurality of components associated with a production run or lot. Moreover, it may be possible to very accurately monitor and continually assess the health of individual components, predict their remaining lives, and consequently estimate the health and remaining useful lives of systems that employ them.
Mechanical systems wear or change over time, which affects the performance of these mechanical systems. Conventional predictive models can estimate the wear of a mechanical system. These models can be updated with information regarding the longevity of various components of the mechanical system using data collected over a system's life as input to statistical models. Theoretical models of the mechanical system which use physics or engineering information to build a model using test data. Such modeling is based on understanding how a system operates and progresses to a failure via knowledge, for example, of material properties and response to loading. However, conventional predictive models are confined to known systems and their component longevity.